Field of the Invention
The present embodiments herein relate to the field of supervision and control of wide-area power systems, and more particularly, to a rule-based data-driven system and method for wide-area fault detection in power systems.
Discussion of the Related Art
Voltage stability studies have been investigated by researchers in the past as several blackouts have been caused or accompanied by voltage instability phenomenon. However, with the advent of Wide-Area Measurement System (WAMS) technology, also known as the synchrophasor technology, as wide area system information is available in real time in the form of voltage and current phasors, improved algorithms can be developed that can make optimum utilization of this bank of system information to efficiently monitor and in real time, control the power system so as to avert a voltage unstable situation that can possibly lead to a blackout.
The basic component of WAMS is a sophisticated digital recording device called the Phasor Measurement Unit (PMU). PMUs have the ability to record and communicate GPS-synchronized, high sampling rate (6-60 samples/sec), dynamic power system data including the magnitude and phase angle of voltage and current, frequency and other parameters, which are transferred to a data control center. Because PMU data are more widely available in near real-time, it can provide unique insights into the global operation of the power grid. It can be used for: 1) stability monitoring and assessment; 2) preventive and emergency control; 3) increasing transmission capability of existing assets; and 4) fault detection.
The greatest threat to power systems is system faults which could occur at any voltage level. Rapid growth of power systems has resulted in a large increase in the number of transmission lines in operation and their total length. These lines experience faults due to various reasons. On overhead line systems, the majority of short-circuit faults, typically 80-90%, tend to occur on overhead lines and the rest on substation equipment and bus bars combined. To expedite repairs and fast restoration of power supply, it is important that the location of a fault is either known or can be estimated with reasonable accuracy. Extensive research work has been conducted on fault detection using PMU data, but the majority of the previous research focuses on fault detection for transmission lines. Either single terminal data or double terminal data of a transmission line are used for fault detection on a particular line and the location of a fault point is usually calculated from the voltage and current measurements at line terminals. However, fault detection methods for a wide-area grid have not received the same amount of attention.
Power grid intelligent fault diagnosis, detection and characterization are important functions for future smart grid. The current practice of electric utilities is to depend on alarms of relays and circuit breakers at control centers. A large number of alarms are usually received in a control center due to power system faults. The operators have to process these alarm data to get the required information about the faults. To improve such situation and provide operators fast and accurate knowledge of the fault condition of the system in order to allow them handle a potential crisis in a timely manner, fault detection and characterization algorithms developed from the real-time synchrophasor data can realize much better protection and control of tomorrow's power grid.
There is currently limited research reported on fault detection methods using synchrophasor data including all of the following features: identify fault type, determine the fault location on a bus, and/or determine fault location on a long transmission line. Now with the power grid gradually becoming “smarter,” there is a need in the industry for developing a new voltage wide-area fault real-time event detection tool using a data-driven approach that is able to monitor the wide area voltage stability condition of a power system's synchrophasor data and then take fast and suitable wide area coordinated control actions in real time to avoid a possible voltage collapse. The embodiments herein address such a need by way of dimensionality reduction analysis through adaptive training to realize real-time event detection using a data-driven approach for effectively processing of such synchrophasor data.